! Abstract Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of death worldwide with a devastating socio-economic burden impacting more than three million individuals per year in the US. The primary environmental risk factor in the susceptible population is smoking, which causes an exaggerated inflammatory response. However, many factors including several genetic risk variants substantially influence the susceptibility. Twin-based studies show that families with emphysema have a higher risk for the disease. The two different major phenotypes of COPD are small airway remodeling (airway disease) and alveolar destruction (emphysema). Although these two major phenotypes result in a similar deficiency in global lung function, the relationship between them is complicated and likely involves feedback mechanisms. Developing an objective method to characterize lung phenotypes is critical since treatment candidates vary based on phenotype. Measurements from High-Resolution Computed Tomography (HRCT) images are increasingly used to describe COPD since they can quantitatively describe the contribution of the phenotypes. To discover the genetic risk variants, Genome-Association Studies (GWAS) have focused on either the physiological lung function or a simple threshold-based measurement from lung CT, neither of which fully characterizes phenotypic subtypes or the distribution pattern of disease. The proposed studies will take advantage of the rich image and genetic data jointly to build a genetically-informed imaging biomarker to characterize each patient. For each patient, our method summarizes the CT image to a vector representation that accurately describes the severity of the disease. Also, a method to link the representation back to the genetic risk variants will be developed. If successful, these methods can be used to monitor the efficacy of treatment or progression of the disease using imaging data. Successful execution of the second aim will result in better understanding of the etiology of different disease subtypes and discovery of novel genetic pathways that could be used as potential drug targets. Furthermore, the patient representation enables the use of image data to construct a more powerful model to predict the so-called acute exacerbation event. Predicting the exacerbations is clinically important since they cause further damage to the lung. In Aim 1, we develop and implement a novel image biomarker that is mutually informed by imaging and genetic data from each patient. Our statistical method in Aim 2 elucidates the underlying genetic pathways behind the abnormal anatomical variations explained by the biomarker. We validate our method on data from 10,300 patients in the COPDGene dataset. !